Receiver for wireless telecommunication stations and method

ABSTRACT

The present invention is directed to an improved telecommunication receiver for receiving wireless multi-path communication signals. A novel RAKE receiver and a time diverse integration system for the calculation of the relative power of received signal samples are provided. Preferably, the receiver is embodied in a UE or base station of a CDMA wireless telecommunication system, such as a 3GPP system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/304,403, filed Nov. 26, 2002 now U.S. Pat. No. 6,748,009, whichclaims the benefit of U.S. Provisional Application No. 60/356,231, filedFeb. 12, 2002, which are incorporated by reference as if fully setforth.

FIELD OF THE INVENTION

The present invention relates to wireless communication systems. Morespecifically, the present invention relates to the reception ofcommunication signals in wireless communication systems.

BACKGROUND

Signal synchronization is important in wireless telecommunication. Inmodem systems, there are various levels of synchronization, such as,carrier, frequency, code, symbol, frame and network synchronization. Ateach level, synchronization can be divided into two phases: acquisition(initial synchronization) and tracking (fine synchronization).

A typical wireless communication system, such as specified in the 3rdGeneration Partnership Project (3GPP), sends downlink communicationsfrom a base station to one or a plurality of User Equipments (UEs) anduplink communications from UEs to the base station. A receiver withineach UE operates by correlating, or despreading, a received downlinksignal with a known code sequence. The code sequence is synchronized tothe received sequence in order to get the maximal output from thecorrelator.

A receiver may receive time offset copies of a transmitted communicationsignal known as multi-path. In multi-path fading channels, the signalenergy is dispersed over a certain amount of time due to distinct echopaths and scattering. To improve performance, the receiver can estimatethe channel by combining the multi-path copies of the signal. If thereceiver has information about the channel profile, one way of gatheringsignal energy is then to assign several correlator branches to differentecho paths and combine their outputs constructively. This isconventionally done using a structure known as a RAKE receiver.

Conventionally, a RAKE receiver has several “fingers”, one for each echopath. In each finger, a path delay with respect to some reference delay,such as the direct or the earliest received path, must be estimated andtracked throughout the transmission. The estimation of the path'sinitial position in time may be obtained by using a multi-path searchalgorithm. The multi-path search algorithm does an extensive searchthrough correlators to locate paths with a desired chip accuracy. RAKEreceivers are able to exploit multi-path propagation to benefit frompath diversity of transmitted signals. Using more than one path, or ray,increases the signal power available to the receiver. Additionally, itprovides protection against fading since several paths are unlikely tobe subject to a deep fade simultaneously. With suitable combining, thiscan improve the received signal-to-noise ratio, reduce fading and easepower control problems.

In the context of mobile UEs, due to their mobile movement and changesin the scattering environment, the delays and attenuation factors usedin the search algorithm change as well. Therefore, it is desirable tomeasure the tapped delay line profile and to reallocate RAKE fingerswhenever the delays have changed by a significant amount.

An important design problem of a RAKE receiver is how to accuratelysearch and find multiple signal paths. There are several key parametersto be optimized for the receiver system, such as mean acquisition time,optimum threshold setting, probabilities of detection and false alarm,etc. One problem with a RAKE receiver is that the paths can disappear ormay not be detected by a RAKE location process. Therefore, there existsa need for an improved receiver.

Another severe design problem of a RAKE receiver is that it is notalways possible to separate the received energy into components due todistinct multipath components. This may happen, for example, if therelative delays of the various arriving paths are very small compared tothe duration of a chip. Such situations often arise in indoor and urbancommunication channels. The problem is often referred to as the “FatFinger Effect.”

While there exist techniques for demodulating the data from Fat fingers,in order to apply such techniques the received energy belonging to a Fatfinger must be identified. Unfortunately, typical RAKE correlators aredesigned to search for distinct single-path components in a multipathchannel and are unable to perform this identification. Thus, thereexists a need for a receiver capable of identifying the Fat fingers.

SUMMARY

The present invention is directed to an improved telecommunicationreceiver for receiving wireless multi-path communication signals. Anovel RAKE receiver and a time diverse integration system for thecalculation of the relative power of received signal samples areprovided. Preferably, the receiver is embodied in a UE or base stationof a CDMA wireless telecommunication system, such as a 3GPP system.

In one aspect of the invention, the station has a receiver forprocessing communication signals, which includes a RAKE receiver havingup to a predetermined number of RAKE fingers, for assigning andcombining a plurality of different signal paths of receivedcommunication signals. In one example, the maximum number of RAKEfingers is five (5) of which up to one is a Fat finger. A Fat finger ofthe RAKE receiver implements a Fat finger demodulation algorithm that,for example, may be a conventional Adaptive Filter.

The receiver has a RAKE locator that determines signal paths based onwindows defined by groups of consecutive signal samples. Windows aredefined where samples within a window exceed a first power threshold.The RAKE locator designates a number of such windows, up to the numberRAKE fingers, as candidate windows based on relative power of thesamples within the determined windows.

Preferably, the RAKE locator defines windows based on a window powerlevel determined by summing power levels of its group of samples. Awindow is defined when its power level exceeds the first powerthreshold. Preferably, the RAKE locator designates windows as candidatewindows based on windows having the highest power levels. However, awindow is not designated if it is too close to another candidate window,i.e., if more than a specified number of samples are included in anotherwindow having a higher power level. For example, each window can containa group of 21 samples and the candidate windows can have no more than 16common samples so that the candidate windows are separated from eachother by at least 5 consecutive samples.

Window search circuitry analyzes candidate windows to determine if thepower of samples of the candidate windows exceeds a second threshold.The window search circuitry designates a Fat finger candidate windowwhen at least one of the candidate windows has a selected number ofcandidate samples that exceed the second threshold. Preferably, thewindow search circuitry designates only one Fat finger candidate window,that being the candidate window having the greatest power level thatalso has a selected number, preferably four (4), of candidate sampleshaving power levels exceeding the second threshold. Candidate samplesare those samples remaining after pruning consecutive samples thatexceed the second threshold.

A RAKE finger allocator assigns candidate windows for processing byeither a conventional type of RAKE finger or a Fat RAKE finger such thatcandidate windows that are not designated as a Fat finger candidatewindow are each assigned to a different conventional RAKE finger.Preferably, the RAKE finger allocator assigns any candidate windowdesignated as a Fat finger candidate window to a Fat RAKE finger.

Methods for processing communication signals using a RAKE receiverhaving up to a predetermined number, for example five (5), of RAKEfingers, which combines a plurality of different signal paths ofreceived communication signals, are provided. Signal paths aredetermined based on windows defined by groups of consecutive signalsamples in which samples within a window exceed a first power threshold.Up to the predetermined number RAKE fingers of such windows aredesignated as candidate windows based on relative power of the sampleswithin the determined windows. Candidate windows are analyzed todetermine if the power of samples of the candidate windows exceeds asecond threshold. A Fat finger candidate window is designated when atleast one of the candidate windows has a second predetermined number ofcandidate samples exceeding the second threshold. Candidate windows areassigned for processing by either a first type of RAKE finger or adifferent second type of Fat RAKE finger such that candidate windowsthat are not designated as a Fat finger candidate window are eachassigned to a different RAKE finger of the first type.

Preferably, windows are defined which have a power level, determined bysumming power levels of its group of samples, which exceeds the firstpower threshold and candidate windows are designated based on windowshaving the highest power levels. However, a window is not designated asa candidate window if more than a specified number of samples areincluded in another window having a higher power level. For example,each group of samples can contain 21 samples and the specified numbercan be set as 16 such that only windows separated from each other by atleast 5 consecutive samples are designated as candidate windows.

Preferably, only up to one Fat finger candidate window is designated,being the candidate window having the greatest power level which alsohas the selected number of candidate samples having power levelsexceeding the second threshold. Candidate samples are samples remainingafter pruning consecutive samples that exceed the second threshold.

Preferably, any candidate window designated as a Fat finger candidatewindow is assigned to a Fat RAKE finger that comprises an AdaptiveFilter.

In a second aspect of the invention, the receiver is configured toprocess communication signals based in part on relative power of signalsamples where relative power is calculated as a function of valuescorresponding to time diverse signal samples. A buffer is provided whichstores at least values r(r) that correspond to signal samples S_(r),which define a set R of samples. R is a subset of X consecutivelyreceived signal samples S₀ through S_(X−1) that correspond to valuesr(0) through r(X−1). The number of elements of subset R is less than Xsuch that R contains at least two mutually exclusive subsets ofconsecutive samples {S₀ through S_(i)} and {S_(j) through S_(x−1)}.Accordingly, R does not include sample S_(i+1) or S_(j−1). Forconvenience the buffer may store all values r(0) through r(X−1), but asubstantially smaller buffer can be used if only the time diversesubsets of values represented by sample set R are stored.

A processor is operatively associated with the buffer for calculatingrelative sample power based on values r(r) that correspond to signalsample elements S_(r) of the selected subset R of X consecutivelyreceived signal samples. Values of samples not contained in R, such asvalues r(i+1) or r(j−1) that correspond to signal sample elementsS_(i+1) and S_(j−1), respectively, are not used in the calculation.Accordingly, relative power is calculated based on sample seriesrepresenting at least two diverse time intervals.

Preferably, the processor is configured to calculate relative powerutilizing a function based on an index set I comprised of mutuallyexclusive subsets of positive integers, such that, for each subset of I,a corresponding subset of R is utilized in calculating relative power.

Each pair of consecutive samples represents a sampling time interval tthat corresponds to the sampling rate used in obtaining samples of areceived signal. Preferably, at least two mutually exclusive subsets ofthe X consecutive samples exist that contain at least consecutivesamples {S_(i+1) through S_(i+51)} and {S_(j−51) through S_(j−1)},respectively, and do not contain any elements of subset R. In such case,subset R is defined by at least three mutually exclusive subsets ofconsecutive samples, which represent groups of consecutive samplesmutually offset in time by at least 50 times t.

Preferably, the processor is configured to calculate correlation powerP_(k) ^(PN) between a PN scrambling sequence and a received signal for asample S_(k) based on:$P_{k}^{P\quad N} = {\sum\limits_{m \in I}{{\sum\limits_{n = 0}^{N - 1}\quad{{r( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}$where N is a predefined constant and c(·) represents valuescorresponding to PN scrambling sequences. In order to limit processingtime, index set I is preferably defined by no more than 150 elements. Inone example, the index set I equals {0-9, 50-69, 100-199}, N is 256.This results in R being defined by three corresponding mutuallyexclusive subsets of consecutive samples that represent groups ofsamples mutually offset in time by more than 5000 times t.

A RAKE finger allocation block preferably includes the buffer andassociated processor configured for time diverse integration so thatcorrelation powers of samples S_(k) are calculated in the allocationblock on a time diverse integration basis. However, implementation oftime diverse integration can be similarly applied to other componentswhere relative signal sample power is calculated.

Other objects and advantages of the invention will be apparent to thoseof ordinary skill in the art from the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an initial Fat finger and RAKE fingerallocation processors in accordance with the teachings of the presentinvention.

FIG. 2 is an illustration of frame and slot structures of P-SCH, S-SCHand CPICH channels of a 3GPP system.

FIG. 3 is a block diagram of a Fat finger allocation processor.

FIG. 4 is a graphical illustration of a threshold comparison blockprocess.

FIG. 5 is a graphical illustration of a window search block process.

FIG. 6 is a graphical illustration of a Fat finger location blockprocess.

FIG. 7 is a Fat finger allocation flowchart.

FIG. 8 is a block diagram of a RAKE finger allocation processor.

FIG. 9 is a graphical illustration of a first rank filter block process.

FIG. 10 is a graphical illustration of a RAKE finger detection blockprocess.

FIG. 11 is a graphical illustration of a second rank filter blockprocess.

FIG. 12 is an illustration of a post-detection structure.

FIG. 13 is a graph of detection probability (P_(D)) of a single pathcase in an AWGN channel with various SNRs.

FIG. 14 is a graph of detection probability (P_(D)) of a first path in amulti-path fading channel (Case 1) with various SNR and a secondthreshold η₂.

FIG. 15 is a graph of detection probability (P_(D)) of a second path inthe multi-path fading channel (Case 1) with various SNR and the secondthreshold η₂.

FIG. 16 is graph of probability of false alarm (P_(FA)) with respect tothe second threshold η₂.

FIG. 17 is a graph of the detection probability (P_(D)) of a first pathin a multi-path fading channel (Case 5) with the various SNR and thesecond threshold η₂.

FIG. 18 is a graph of detection probability (P_(D)) of a second path inthe multi-path fading channel (Case 5) with various SNR and the secondthreshold η₂.

FIG. 19 is a graph of probability of false alarm (P_(FA)) with respectto the second threshold η₂.

FIG. 20 is a block diagram of a RAKE management structure.

FIG. 21 is a RAKE relocation flowchart.

FIG. 22 is a graphical illustration of a path search process.

FIG. 23 is a graphical illustration of a path verification process.

FIG. 24 is an illustration of a path selector process.

FIG. 25 is a graph of probability of detection for multi-path fading(Case 1).

FIG. 26 is a graph of the probability of detection multi-path fading(Case 1).

FIG. 27 is a graph of the probability of detection multi-path fading(Case 1).

FIG. 28 is a graph of birth-death propagation sequence.

FIG. 29 is a graph of primary synchronization channel (PSC) response.

FIG. 30 is a graph of common pilot channel (CPICH) responses.

FIG. 31 is a graph of detection probability of the first path (Case 1).

FIG. 32 is a graph of detection probability of the second path (Case 1).

FIG. 33 is a graph of false alarm probability (Case 1).

FIG. 34 is a graph of detection probability of the first path (Case 5).

FIG. 35 is a graph of detection probability of the second path (Case 5).

FIG. 36 is a graph of false alarm probability (Case 5).

TABLE OF ACRONYMS 3GPP 3rd Generation Partnership Project AF AdaptiveFilter AWGN Additive White Gaussian Noise BCH Broadcast Channel CDMACode Division Multiple Access CFAR Constant False Alarm Rate CPICHCommon Pilot Channel FRF First Rank Filter HGC Hierarchical GolayCorrelator MS Mobile Station P-CCPCH Primary Common Control PhysicalChannel PN Pseudo Noise PSC Primary Synchronization Code P-SCH PrimarySynchronization Channel SSC Secondary Synchronization Code S-SCHSecondary Synchronization Channel SNR Signal to Noise Ratio UE UserEquipment WG4 Working Group 4 (of 3GPP)

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is described below in the context of acommunication system made in accordance with current 3rd GenerationPartnership Project (3GPP) specifications. In such systems, CDMAcommunication signals are transmitted within system time frames at aspecified chip rate. Wireless communication occurs between UserEquipment (UE) and base stations known as Node Bs. Both UEs and Node Bstransmit and receive wireless communication signals and, accordingly,are generically referred to as wireless transmit receive units (WTRUs).In conjunction with the receiving equipment of either type ofcommunication station, i.e. UE or Node B, a RAKE receiver in accordancewith the teachings of the present invention may be advantageouslyemployed to improve performance by efficient processing of receivedmulti-path communication signals. Except as specified differentlyherein, the preferred processes for cell search, code acquisition, andsynchronization is in accordance with current 3GPP specification.

In order to evaluate the performance of the RAKE receiver of the presentinvention, its performance was evaluated in view of various simulations.These included simulations using an Additive White Gaussian Noise (AWGN)channel and two different channels as specified by 3GPP Working Group 4(WG4), known in the art as WG4 Case 1 and WG4 Case 5 channels underversion 3.2 of 3GPP Technical Specifications.

The inventors have recognized that a propagation channel impulseresponse may comprise a “Fat” finger path in combination with standardRAKE finger paths. A Fat finger path represents multiple paths close toeach other and each standard RAKE finger path represents a single pathseparated by at least one chip interval from other paths. Typically, achannel response does not have more than one “Fat” finger path so thepreferred embodiment described herein focuses on detecting only one“Fat” finger. The teachings of the invention, however, are equallyapplicable for detecting a plurality of “Fat” fingers.

In the present invention, a RAKE locator continuously looks for Fatfinger and RAKE finger paths. The Fat finger path is assigned to a Fatfinger comprised of a demodulating algorithm/system capable ofdemodulation the Fat finger path and each individual multi-pathcomponent is assigned to a standard RAKE finger, respectively, with atracking mechanism present in each of these fingers. Such standard RAKEfingers that are separated by at least one chip width represent the RAKEreceivers of the prior art. One example of a demodulatingalgorithm/system capable of demodulation the Fat finger path is anAdaptive Filter (AF).

The RAKE locator, shown in FIG. 1, plays an important role as theconnection between the searching mechanism (Cell Search) and the RAKEreceiver. After code phase acquisition has been established by a cellsearch process, the RAKE fingers are associated with detected codephases. A detected code phase corresponds to a time delay due tomulti-path in a received radio channel. Since the delays of the channelmulti-path are often non-stationary, it is necessary to continuouslylook for new multi-path components in the channel. The code phases dueto multi-paths are then allocated to the RAKE receiver for demodulation.This coarse synchronization for each RAKE finger is then finesynchronized by a code tracking mechanism in each individual RAKEfinger. The code phases allocated to a RAKE finger may disappear as amobile UE moves and the delay profile of the received channel changes.These fingers are then de-allocated from the RAKE receiver and new codephases are reallocated from a RAKE locator. This process is described inthe RAKE Reallocation system set forth hereinafter.

FIG. 1 shows an overall block diagram for a RAKE locator designed for a3GPP system that includes initial FAT finger and RAKE finger allocationprocessors. The locator cooperates with a 3GPP initial cell searchalgorithm to accelerate the speed of resolving the multi-paths.

During synchronization, a mobile station (UE) searches for a basestation (BS) from which it receives the highest signal power. In apreferred embodiment, the cell search block determines the downlinkscrambling code and frame synchronization of that base station inaccordance with current 3GPP specifications. After the scrambling codehas been identified, the RAKE receiver continuously requires theknowledge of the relative delay or code phase of each multi-path, ormulti-path group for a Fat finger, of the radio channel.

During a first step of the cell search procedure, the UE uses the codeof a Primary Synchronization Channel (P-SCH) to acquire slotsynchronization to a cell. This is typically done with a single matchedfilter matched to the P-SCH channel. The code used by the P-SCH iscommon to all cells. The slot timing of the cell can be obtained bydetecting peaks in the matched filter output.

During a second step of the cell search procedure, the UE uses aSecondary Synchronization Channel (S-SCH) to find frame synchronizationand identify the code group of the cell found in the first step. This isdone by correlating the received signal with all possible secondarysynchronization code sequences, and identifying the maximum correlationvalue. Since the cyclic shifts of the sequences are unique, the codegroup as well as the frame synchronization is determined.

During a third and final step of the cell search procedure, the UEdetermines the exact primary scrambling code used by the found cell. Theprimary scrambling code is typically identified through symbol-by-symbolcorrelation over a common pilot channel (CPICH) with all codes withinthe code group identified in the second step. After the primaryscrambling code had been identified, the Primary Common Control PhysicalChannel (P-CCPCH) can be demodulated, and the system and cell specificinformation can be read from a Broadcast Channel (BCH) that is carriedon the P-CCPCH. FIG. 2 is an exemplary illustration of the time frameand slot structures of P-SCH, S-SCH and CPICH.

The performance of the cell search algorithm has major impacts on theRAKE locator. If the cell search fails, the wrong PN scrambling code isassigned to the RAKE locator and consequently the RAKE locator generatesa false path indication. Accordingly, the RAKE locator acts to verifythe cell search algorithm and to remove false detections.

FIG. 3 shows a block diagram of the Fat finger allocation processor.This processor includes three main blocks: Threshold Comparison block,Window Search block and FAT Finger Location block. The ThresholdComparison block preferably compares hierarchical Golay correlator (HGC)output in accordance with current 3GPP specification with a thresholdη₁, to suppress noise components. The Window Search block selects apredetermined number, such as five (5) of the best window candidatescontaining the largest moving average window powers. Each designatedwindow then becomes a candidate for one of the RAKE fingers. The FatFinger Location block finds the window containing the maximum power.

The first threshold η₁, used in the Threshold Comparison block, isproportional to the average noise power in the P-SCH. A second thresholdη₂ is used in the Fat Finger Location block that is based on the averagenoise power in the CPICH. The two thresholds, η₁, and η₂, determine thedetection probability and false alarm probability of the receiversystem.

Using the threshold comparisons, the FAT Finger Location is assigned toa window identified with a starting timing index τ_(w). This index isfed to an Adaptive Filter (AF), which comprises the “Fat” finger of theRAKE receiver, for further processing. The role of the FAT fingerallocation process is to provide a window location and verify the powersof the FAT finger paths.

FIG. 4 illustrates the process of the threshold comparison block. Thetask of the Threshold Comparison block is the pre-detection and searchfor the true code phase in the P-SCH channel. The cell search step 1provides a slot boundary that is a value in the range 0 to 5119 (a slotat two samples per chip). Once the slot boundary is given, a window of±200 samples with half-chip sampling interval around a slot boundaryproduces a total of 401 samples. The value of ±200 is preferred becausethe maximum delay spread of the radio channel is assumed to be±100T_(c).

Since the P-SCH is common to all cells, the input to the ThresholdComparison block contains values corresponding to path energies from alldetectable base stations. Therefore post-detection is required to verifywhich signal belongs to the desired base station and to suppress othersignals. To maintain a low probability of false alarm, it is necessaryto determine an appropriate threshold η₁. This threshold should beproportional to the average noise power. If η₁, is too low, theprobability of false alarm may be unacceptably high. If η₁ is too high,the detection probability may be too low. This is a trade-off in theselection of η₁.

The input to the Threshold Comparison block, i.e. the integrated HGCoutput of the cell search step 1, is compared to the threshold η₁ toseparate the samples above and below the threshold. The output of theThreshold Comparison block is{tilde over (P)} ^(HGC)=max(P _(i) ^(HGC)−η₁,0), −200≦i≦200.  (1)where i=0 represents the slot boundary. The threshold is adaptivelychanged by the average noise power σ_(n) ^(HGC) such thatη₁=ασ_(n) ^(HGC),  (2)with a proper scaling factor α.

The main task of the Window Search block is to find a predeterminednumber of candidate windows containing the largest powers with a maximumallowed overlap. The number of candidate windows corresponds to thenumber of available RAKE fingers, which in this example is five (5). Thewindow size, for example, is 21 samples. The powers of the movingaverage (MA) sliding windows can be computed as $\begin{matrix}{{P_{i}^{window} = {\sum\limits_{n = 0}^{20}{\overset{\sim}{P}}_{i + n}^{HGC}}},{{- 200} \leq i \leq 180},} & (3)\end{matrix}$where the power {tilde over (P)}_(i) ^(HGC) is given by Equation (1).Then the window powers are ranked in descending order such thatP ⁽¹⁾ ≧P ⁽²⁾ ≧P ⁽³⁾≧ . . . ,  (4)with P⁽¹⁾=max(P_(i) ^(window)). For finding five windows, the preferredrequirements are given by

-   -   1. The window candidates P⁽¹⁾→P⁽⁵⁾ should all exceed a minimum        window power P_(min), which is a design parameter, i.e.,        P ⁽¹⁾ ≧P ⁽²⁾ ≧ . . . P ⁽⁵⁾ ≧P _(min).  (5)    -   2. The window candidates are separated by at least 5 samples,        i.e., for (j)th candidate P^((j))=P_(k) ^(window) and (j+1)th        candidate P^((j+1))=P_(l) ^(window) should satisfy the condition        if P _(k) ^(window) ≧P _(l) ^(window), then |k−l|≧5.  (6)

If requirement 1 is not met, fewer than five window candidates aredetermined and fewer than five fingers of the RAKE receiver areassigned, unassigned fingers remaining idle. If requirement 2 is notmet, the candidate window having the highest power is used and thosewithin 5 or less samples are not used.

FIG. 5 illustrates the window search procedure. First, compute P_(i)^(window) as in Equation (3). Second, sort P_(i) ^(window) as descendingorder. Third, select the first five candidates that are separated by atleast 5 samples. For convenience of illustration, only the first sevensamples are indicated in each window shown in outline. As noted abovethe preferred window size is 21 samples.

If the window candidates overlap each other (for example, {P⁻¹⁹³^(window),P⁻¹⁹⁸ ^(window)} and {P⁻⁵ ^(window),P₁ ^(window)} in FIG. 5,these regions may be saved in the buffer. In the FAT finger allocationblock this is used to reduce the integration time for calculatingcorrelation powers P_(k) ^(PN) between the PN scrambling sequence andthe received signal. For example, assume that the first window candidatehas 5 as a starting point and the second window candidate has a startingpoint 11. The overlapping samples for the 21 sample size windows, are 11to 25 (16 samples). In this region, it is better to prevent doublecalculations for P_(k) ^(PN).

The FAT Finger Location block performs the post-detection processutilizing the CPICH channel. Since CPICH is unique for each cell in agiven area, the correlation over the CPICH gives true code phase for aspecific cell. For example, assume that three base stations areavailable in the radio channel to a UE. If the UE is communicating withBS1, then the correlation over the CPICH channel emphasizes the codephases of the BS1 only and suppresses the code phases of BS2 and BS3.The power of the correlation between the received signal and the PNscrambling sequences are computed as $\begin{matrix}{{P_{k}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}},{{- 100} \leq k \leq 100}} & (7)\end{matrix}$where the sequences of r(·) and c(·) represent the received signal andPN scrambling sequence, respectively. Typical values of J and N are J=50(5 slots), N=256 (one symbol length in chips). As currently specified in3GPP, the sampling rates of the received signal and the PN scramblingsequences are different. The sampling rate of the received signal isT_(c)/2. However the PN scrambling sequence is sampled at T_(c)interval. Therefore (7) can be modified as $\begin{matrix}{{{\begin{matrix}{{P_{2k}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r_{even}( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}},} \\{{P_{{2k} + 1}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r_{odd}( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}},}\end{matrix} - 200} \leq k \leq 200},} & (8)\end{matrix}$where r_(even)(·) and r_(odd)(·) represent even and odd samples of thereceived signal respectively. To simplify the equation (7), let${x(m)} = {{\sum\limits_{n = 0}^{N - 1}{{r( {m,n} )}{c^{*}( {m,n} )}}} = {{a(m)} + {j\quad{{b(m)}.}}}}$

The absolute value operation can be approximated as|x(m)|≈max(|a(m)|,|b(m)|)+0.5 min(|a(m)|,|b(m|).  (9)

Then, equation (7) is simplified with the help of (9) such that$\begin{matrix}\begin{matrix}{P_{k}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}} \\{= {{\sum\limits_{m = 0}^{J - 1}{{x(m)}}} = {\sum\limits_{m = 0}^{J - 1}\sqrt{{a(m)}^{2} + {b(m)}^{2}}}}} \\{\approx {\sum\limits_{m = 0}^{J - 1}{\lbrack {{\max( {{{a(m)}},{{b(m)}}} )} + {0.5{\min( {{{a(m)}},{{b(m)}}} )}}} \rbrack.}}}\end{matrix} & (10)\end{matrix}$

Furthermore, since the different sampling rates between the receivedsignal and the scrambling sequence have to be taken into account, (10)can be expressed as $\begin{matrix}\begin{matrix}{P_{2k}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r_{even}( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}} \\ \Rightarrow{P_{2k}^{P\quad N} \approx {{\sum\limits_{m = 0}^{J - 1}{\max( {{{a_{even}(m)},{b_{even}(m)}}} )}} +}}  \\{{0.5{\min( {{{a_{even}(m)},{b_{even}(m)}}} )}},} \\{P_{{2k} + 1}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r_{odd}( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}} \\ \Rightarrow{P_{{2k} + 1}^{P\quad N} \approx {{\sum\limits_{m = 0}^{J - 1}{\max( {{{a_{odd}(m)},{b_{odd}(m)}}} )}} +}}  \\{0.5{{\min( {{{a_{odd}(m)},{b_{odd}(m)}}} )}.}}\end{matrix} & (11)\end{matrix}$

If the correlation power P_(k) ^(PN) is greater than the secondthreshold η₂, then the code phase is accepted as a true path. The secondthreshold η₂ is proportional to the average noise power, i.e.,η₂=βσ_(n) ^(PN),  (12)where β is a scaling factor and σ_(n) ^(PN) is the average noise powergiven by $\begin{matrix}{\sigma_{n}^{PN} = {\sum\limits_{m = 0}^{J - 1}{{{\sum\limits_{n = 0}^{N - 1}{{r_{even}( {{N\quad m} + n} )}{c_{AUX}^{*}( {{N\quad m} + n} )}}}}.}}} & (13)\end{matrix}$

Here c_(AUX)(·) represents an auxiliary PN scrambling code. Equation(13) is also simplified as (10) using the modified absolute valueoperator.

If the FAT finger is assigned at the point τ_(w), then the powers {tildeover (P)}_(i) ^(HGC) in (1) reset to be zero for further processing inRAKE allocation so that an individual standard RAKE finger is notassigned at the FAT finger location.{tilde over (P)} _(i) ^(HGC)=0, i=τ _(w), τ_(w)+1, . . . ,τ_(w)+20.  (14)

FIG. 6 shows the FAT Finger Location block process. The upper part showsthe selection of the best five window candidates. This process is thepre-detection part. The window indices are fed into the post-detectionpart corresponding to the lower part. The lower part computes thecorrelation powers using equation (11). Preferably, a Fat finger isassigned when a selected window has a minimum number of non-consecutivesamples above the second threshold.

If the FAT finger is not assigned, the output of the ThresholdComparison block is the input of the RAKE finger allocation processor.In this case non-consecutive measurements are preferably pruned to makesure that the paths are separated by at least one-chip. This may beperformed, for example, by starting with the highest sample in the givenwindow, removing the adjacent samples, leaving the next-to-adjacentsamples, removing the samples adjacent to the ones just kept, etc.

FIG. 7 is a flowchart illustrating a preferred Fat finger allocationmethod. The parameters in this flowchart are:

-   -   P_(min): The minimum average window power.    -   N_(c): The number of samples above the second threshold η₂.    -   N_(low): The lowest allowed number of samples above the second        threshold η₂.    -   N_(req): The required number of samples above the second        threshold η₂.    -   N′_(c): The number of samples above the second threshold after        pruning close components.    -   N_(acc): The acceptable number of samples above the second        threshold after pruning. Notice that the multi-path width N_(w)        is measured and sent to the Fat Finger that assigns a matching        number of taps.

The number of samples above the second threshold (N_(c)) and the numberof samples above the threshold after pruning (N′_(c)) is counted.Finally, the right-most window is designated as a FAT finger because italone meets the criterion for a minimum value of N′_(c), which ispreferably set at 4. That is, the FAT finger is only used if there areat least 4 samples above the threshold, each separated by at least onechip in a selected window.ponents.

-   -   N_(acc): The acceptable number of samples above the second        threshold after pruning. Notice that the multi-path width N_(w)        is measured and sent to the Fat Finger that assigns a matching        number of taps.

The number of samples above the second threshold (N_(c)) and the numberof samples above the threshold after pruning (N′_(c)) is counted.Finally, the right-most window is designated as a FAT finger because italone meets the criterion for a minimum value of N′_(c), which ispreferably set at 4. That is, the FAT finger is only used if there areat least 4 samples above the threshold, each separated by at least onechip in a selected window.

As illustrated in FIG. 7, the FAT finger location process 10 commencesby checking the first candidate in the window to see if its total power(step 1) exceeds the minimum acceptable power. If not, the locationblock tries the next candidate. If there is no candidate satisfying thiscondition, the process goes to step 6. The symbol-by-symbol correlationbetween the input signal r(m, n) and the locally generated PN scramblingcode c(m, n) is then computed (step 2) as follows: $\begin{matrix}\begin{matrix}{{P_{2k}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r_{even}( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}},} \\{{P_{{2k} + 1}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r_{odd}( {{N\quad m} + n} )}{c^{*}( {{N\quad m} + n - k} )}}}}}},}\end{matrix} & (15)\end{matrix}$where J is the number of symbols accumulated and N is the symbol lengthin chips. Then compare with the second threshold η₂. The number ofsamples N_(c) above the threshold η₂ is counted (step 3) and, at step 4,are sorted into one of three different cases:

-   -   (a) Case 1: If N_(c)<N_(low), go to step 1 and try the next        candidate, if any.    -   (b) Case 2: If N_(low)≦N_(c)≦N_(req),        -   (i) Count N′_(c), the number of samples above the second            threshold after pruning samples.        -   (ii) Check if N′_(c) is greater than N_(acc), the acceptable            number of samples after pruning. If yes, assign Fat finger            and go to step 5. If not, go to step 1.    -   (c) Case 3: If N_(c)≧N_(req), assign Fat finger and go to step        5.

To prevent assignment of RAKE fingers in the FAT finger region, resetthe HGC output in the FAT finger region of (1) is reset to zero, i.e.,P _(i) ^(HGC)=0, i=τ _(w), τ_(w)+1, . . . , τ_(w)+20.

Once all of the candidates have been processed, the RAKE fingerallocation process is entered (step 6).

A block diagram of the RAKE finger allocation processor is shown in FIG.8. The RAKE finger processor comprises a first rank filter, a RAKEfinger detector, a second rank filter and an assignor. The First RankFilter ranks the input {tilde over (P)}_(i) ^(HGC) from the cell searchHGC in descending order and selects the M largest samples. These samplesmust be at least 2 samples away from each other. If there is no FATfinger assigned, then the output of threshold comparison block {tildeover (P)}_(i) ^(HGC) is fed directly into the First Rank Filter block.The input of this block is the HGC output power above the threshold η₁as in Equation (1), i.e.:{tilde over (P)}_(i) ^(HGC), −200≦i≦200.  (16)

This block ranks these powers in descending order such that:P _(FRF) ⁽¹⁾ ≧P _(FRF) ⁽²⁾ ≧ . . . ≧P _(FRF) ^((M)),  (17)where P_(FRF) ⁽¹⁾=max({tilde over (P)}_(i) ^(HGC)) and the subscript FRFrepresents First Rank Filter and M is a design parameter. The output ofthis block is not the correlation powers but the time indicescorresponding to their powers, i.e.:[I₁, I₂, . . . , I_(M)]  (18)

These samples are preferably checked to make sure they are separated by2 samples to get the preferred chip duration multi-path resolution andpruned if they are not sufficiently separated. In other words, thesample corresponding to I_(j+1) is pruned if the following relationshipis not satisfied:|I _(j) −I _(j+1)|≧2, j=1,2, . . . , M−1.  (19)

FIG. 9 shows an example where the 8 largest correlation powers areselected, not including the FAT finger region. The indices correspondingto these locations are fed into the RAKE Finger Detection block. TheRAKE Finger Detection block verifies whether the correlation powerscorresponding to the indices provided by Equation (17) are greater thanthe second threshold η₂ in the CPICH power. The correlation powers canbe obtained by: $\begin{matrix}{{P_{k}^{P\quad N} = {\sum\limits_{m = 0}^{J - 1}{{\sum\limits_{n = 0}^{N - 1}{{r( {{256\quad m} + n} )}{c^{*}( {{256\quad m} + n - k} )}}}}}},{k = I_{1}},I_{2},\quad\ldots\quad,{I_{M}.}} & (20)\end{matrix}$where the sampling rates of r(·) and c(·) are different, Equation (20)is modified as: $\begin{matrix}{{{\begin{matrix}{{P_{2k}^{PN} = {\sum\limits_{m = 0}^{J - 1}{\quad{\sum\limits_{n = 0}^{N - 1}{{r_{odd}( {{Nm} + n} )}{c^{*}( {{Nm} + n - k} )}}}}}},} \\{{P_{{2k} + 1}^{PN} = {\sum\limits_{m = 0}^{J - 1}{\quad{\sum\limits_{n = 0}^{N - 1}{{r_{even}( {{Nm} + n} )}{c^{*}( {{Nm} + n - k} )}}}}}},}\end{matrix}\quad k} = I_{1}},I_{2},\cdots\quad,{I_{M}.}} & (21)\end{matrix}$

If the correlation power in Equation (20) is greater than the secondthreshold η₂, (i.e., P_(k) ^(PN)≧η₂), the corresponding code phase isverified as a true path for the detected cell; otherwise the code phaseis not verified as a true path. In the verification mode forpost-detection, the adjacent code phases (left and right) of the givenindices from Equation (18) are also tested to account for clock driftand vehicle motion. FIG. 10 shows the RAKE Finger Detection blockprocess. The upper part illustrates the selection of the largest Msamples, for example eight (8), and their indices, [I₁,I₂, . . . , I₈].The lower part illustrates the verification process that determines Ltrue paths. The adjacent indices of [I₁, I₂, . . . , I₈] are:[(I₁−1,I₁,I₁+1),(I₂−1,I₂,I₂+1), . . . , (I₈−1,I₈,I₈+1)],  (22)and their corresponding powers are:[(P_(I) ₁ ⁻¹ ^(PN),P_(I) ₁ ^(PN),P_(I) ₁ ₊₁ ^(PN)),(P_(I) ₂ ⁻¹^(PN),P_(I) ₂ ^(PN),P_(I) ₂ ₊₁ ^(PN)), . . , (P_(I) ₈ ⁻¹ ^(PN),P_(I) ₈^(PN),P_(I) ₈ ₊₁ ^(PN))]  (23)

Preferably, the largest powers above the second threshold and theirindices of each set in Equations (22) and (23) are selected as a truepath. FIG. 10 illustrates the selection of the indices[I₈+1,I₃,I₇−1,I₁,I₆+1]  (24)as each being verified as a true path in the illustrated example. Inthis case, L is found to be 5 since three of the eight sets in Equations(22) and (23) have no power above the second threshold.

The Second Rank Filter selects the largest K samples out of the Lcandidates where K is the number of RAKE fingers or L, if smaller. Theinput of this block is the correlation powers above the threshold andtheir indices. These powers are ranked in descending order:P ₁ ^(RAKE) ≧P ₂ ^(RAKE) ≧ . . . ≧P _(L) ^(RAKE)  (25)and the corresponding indices from (24) are sorted in Equation (25) as:└I₁ ^(RAKE), I₂ ^(RAKE), . . . , I_(L) ^(RAKE)┘.  (26)

The output of this block is the indices of the K largest samples inEquation (26) which correspond to:└I₁ ^(RAKE), I₂ ^(RAKE), . . . , I_(K) ^(RAKE)┘.  (27)

FIG. 11 illustrates the second rank filter block process. Thecorrelation powers are ranked in descending order such that:P _(I) ₁ ^(PN) ≧P _(I) ₈ ₊₁ ^(PN) ≧P _(I) ₇ ⁻¹ ^(PN) ≧P _(I) ₆ ₊₁ ^(PN)≧P _(I) ₃ ^(PN) →P ₁ ^(RAKE) ≧P ₂ ^(RAKE) ≧P ₃ ^(RAKE) ≧P ₄ ^(RAKE) ≧P ₅^(RAKE).

The indices are also sorted in that order such that:[I₁, I₈+1,I₇−1,I₆+1,I₃]→[I₁ ^(RAKE),I₂ ^(RAKE),I₃ ^(RAKE),I₄ ^(RAKE),I₅^(RAKE)].  (28)

Finally, if K RAKE fingers are available in the receiver system, the Kindices of Equation (28) are assigned as the RAKE fingers as set forthbelow. Less than K fingers may be available where a FAT finger has beenassigned.

The largest RAKE finger path is always assigned to a RAKE receiverfinger, even if it fails to meet the minimum criteria, unless there is aFAT finger assigned. If a RAKE finger, FAT or standard, is assigned,each additional finger path preferably must pass an additional testbefore being assigned to a RAKE receiver finger.

The additional test determines whether the added SNR exceeds someminimum ΔdB. If the current SNR after k fingers are assigned is SNR_(k)dB, the additional finger is assigned if:SNR _(k+1) −SNR _(k)≧ΔdB.  (29)

This is equivalent to comparing the measured linear power of the k+1thcomponent, P_(k+1), to the cumulative power: $\begin{matrix}{{CP}_{k} = {\sum\limits_{j = 1}^{k}\quad{P_{j}.}}} & (30)\end{matrix}$

If P_(k+1)≧(δ−1)CP_(k), then the finger is assigned. Here, δ=10^(0.1Δ).For example, if δ= 1/16, then Δ=0.26 dB. In that case another RAKEreceiver finger is assigned only if a component adds an additional 0.26dB to the total SNR.

A typical radio propagation channel contains reflections caused bybuildings, mountains, and mobile obstacles in the propagation path.These multiple paths generate attenuation and distortion of the signalenergy. The delay profile extends typically from 1 to 2 μs in urban andsuburban areas, although in some cases delays as long as 20 μs or morewith significant signal energy have been observed in mountainousregions. If the time difference of the multi-path components is at least0.26 μs (chip duration), the CDMA RAKE receiver can separate thosemulti-path components and combine them coherently to obtain diversity.The 0.26 μs delay can be obtained if the difference in path lengths isat least 260 ft. The received signal power can drop considerably whenphase cancellation of multi-path reflection occurs. Because of theunderlying geometry causing the fading and dispersion phenomena, signalvariations due to fast fading occur several orders of magnitude morefrequently than changes in the average multi-path delay profile.

There are several techniques to overcome this fading. The firsttechnique is using RAKE fingers allocated to those delay positions onwhich significant energy arrives. The second technique is fast powercontrol and diversity reception. The third technique is coding andinterleaving.

The outputs of the FAT finger allocation and RAKE finger allocationprocessors are used to improve overall system performance. For example,the average acquisition times depend on the detection probability, falsealarm probability, the dwell time, the false alarm penalty time and thenumber of cells to search. Since the average acquisition time is veryimportant for the performance of the acquisition device, it is desirableto optimize all the above parameters. FIG. 12 shows the post-detectionstructure for obtaining cross-correlation power between the PNscrambling sequence and the received signal in the neighborhood of theframe boundary.

In the absence of any a priori information regarding the true code phaseposition, the uncertainty in misalignment between the received PN codeand the local replica of it could be as much as a full code period.Thus, for long PN codes the corresponding time uncertainty to beresolved could typically be quite large. It is typical in practice torequire that the received and local PN code signals be aligned to withinone-half chip period T_(c)/2 before relinquishing control to the finesynchronization tracking system. In accordance with this requirement,the time delay of the local PN code signal would be retarded or advancedin discrete steps. Thus, if T_(U)=N_(U)T_(c) is the time uncertainty tobe resolved, then q=2N_(U)+1 is the number of possible code alignmentswhich, in serial search parlance, are referred to as cells to beexamined during each search through the uncertainty region.

The goal of code acquisition is to achieve a coarse time alignmentbetween the received pseudo noise (PN) code r(m, n) and the locallygenerated code c(m, n) to an accuracy of a fraction of one PN sequencechip. A popular approach to code acquisition is the serial searchtechniques, which correlate the received and locally generated codesequences and then test the synchronization based on either the crossingof a threshold or the maximum correlation. A threshold value isdetermined depending on the signal-to-noise ratio of the matched-filteroutput, and it may be adjusted according to either the noise power orthe partial correlation. A search technique employs both the maximumcriterion as well as the threshold-crossing criterion. The parameters inthis analysis are the following:

-   -   P_(D): Probability of detection when the correct bin is tested    -   P_(FA): Probability of false alarm when an incorrect bin is        tested    -   τ_(d): Dwell time (Integration time) in each cell    -   K: The number of dwell penalty time units    -   q: Total number of cells to be searched

The mean acquisition time {overscore (T)}_(ACQ) is: $\begin{matrix}{{\overset{\_}{T}}_{ACQ} = {\frac{2 + {( {2 - P_{D}} )( {q - 1} )( {1 + {KP}_{FA}} )}}{2P_{D}}\tau_{d}}} & (31)\end{matrix}$where the average dwell time is given by: τ_(d) =JT _(s)=256×JT _(c).  (32)

If 5 slots are used (J=50), then, τ_(d)=3.3 ms. The formula for meanacquisition time in Equation (31) is a function of the probability ofdetection P_(D), probability of false alarm P_(F) and dwell time τ_(d).For a high probability of detection P_(D) and the low probability offalse alarm P_(F): $\begin{matrix}{{\overset{\_}{T}}_{ACQ} \approx {\frac{( {q - 1} )}{2}{\tau_{d}.}}} & (33)\end{matrix}$Since q−1=400, we find {overscore (T)}_(ACQ)≈0.66 sec, where (33) isobtained from (31) by approximating as follows: P_(FA)≈0, P_(D)≈1, q ismuch greater than 1.

In many practical code acquisition systems, the reduction of the falsealarm probability for a given overall acquisition time involves the useof search techniques in conjunction with a verification algorithm. Theverification process alternates with the search process and is startedwhenever an acquisition is declared. The search is then placed on holdduring verification algorithm. A system that employs both search andverification is called a double-dwell system. When properly employed, adouble-dwell search strategy may result in significant speed-up of theoverall search process. A speed-up of approximately a factor of 3 hasbeen observed in simulation.

FIG. 13 shows the detection probability P_(D) of the single path in theAWGN channel with respect to the various SNR. If the input SNR exceeds 4dB, the detection probability is almost 1.0. To get the same performancein a multi-path fading channel, the input SNR must increase up to 15dB-20 dB.

FIG. 14 shows the detection probability of the first path in themulti-path fading channel for WG4 Case 1, wherein there are two pathswith 0 and −10 dB Rayleigh-fading amplitudes at 3 km/h speed. The inputSNR must be increased up to 20 dB to get similar performance compared toFIG. 13. For the first path the detection probability is not muchdifferent with respect to the second threshold η₂.

FIG. 15 shows the detection probability of the second path in themulti-path fading channel. If the second threshold η₂ is low, there is abetter detection probability. For example, if input SNR is 10 dB, thenthe difference of detection probability is 0.23 (23%) when the secondthreshold varies from η₂=1.2σ_(n) ^(PN) to η₂=1.8σ_(n) ^(PN).

FIG. 16 shows the probability of false alarm (P_(FA)) with respect tothe second threshold η₂. It is obvious that if the second threshold isincreased, the probability of false alarm decreases.

There is a trade-off between the probability of false alarm and thedetection probability which is controlled by the second threshold. Ifthe second threshold is decreasing, the probability of false alarm andthe detection probability are both increasing especially for the secondpath, and vice versa. FIG. 16 also shows that if input SNR is highenough, then the second threshold should be adjusted to a high enoughvalue to get a low probability of false alarm.

FIG. 17 shows the detection probability of the first path with variousSNR and the second threshold η₂, for multi-path fading channel Case 5wherein

there are two paths with 0 and −10 dB Rayleigh-fading amplitudes at 50km/h speed. Compared to Case 1 (FIG. 14), the detection probability isincreased from 0.44 to 0.83 with the second threshold η₂=1.2σ_(n) and 5dB input SNR. Notice that the detection probability is almost doubledwhen the speed is increased from 3 km/h to 50 km/h. When the input SNRis higher than 10 dB, the detection probabilities are more than 90%.

FIG. 18 shows the detection probability of the second path (−10 dBamplitude). Compare to Case 1 (FIG. 15), the detection probability isincreased from 0.04 to 0.27 with the second threshold η₂=1.2σ_(n) and 5dB input SNR. Generally, the simulation results show that the detectionprobability is increased as the speed is increased.

To detect the second path more than 90% in any threshold, the input SNRshould be around 20 dB. With low input SNR, the detection probabilityhighly depends on the second threshold. The detection probabilities are0.27, 0.13, and 0.04 with the second threshold η₂=1.2σ_(n), η₂=1.5σ_(n),and η₂=1.8σ_(n), respectively.

FIG. 19 shows the probability of false alarm with respect to the secondthreshold η₂. Compared to the Case 1 (FIG. 16), overall false alarmrates are increased. For example, the false alarm rate is changed from0.2250 to 0.3233 with the second threshold η₂=1.2σ_(n) and 20 dB inputSNR. It is obvious that the threshold η₂ should be high enough to get alow probability of false alarm.

The probability of detection is improved when the speed is increased.However, the probability of false alarm is increased when the speed isincreased with otherwise the same conditions. The second threshold η₂ ispreferably selected to maximize the detection probability and tominimize the probability of false alarm. The proper strategy between theprobability of detection and the probability of false alarm is selectedto optimize the performance of the receiver system.

In order to alleviate the problem of paths disappearing or not beingdetected by the RAKE location process above, the present inventionpreferably utilizes a RAKE relocation process. However, if the FATfinger path disappears or if no FAT finger has been assigned, the RAKElocation process is preferably conducted again after a selected timeinterval.

A RAKE management system implements the relocation process and comprisesthe following processors: Path Searcher, Allocation, Relocation, PathSelector and RAKE Controller. FIG. 20 shows the overall RAKE managementsystem structure.

The RAKE Relocation process shown in FIG. 21, is used to reselect thepath candidates and compare the path candidates with the existing paths.Then, if the powers of the candidates are greater than the powers of theexisting tracks, the current paths are de-allocated and the new pathsare reassigned to the RAKE finger.

The power delay profiles can be found by using the Hierarchical GolayCorrelator (HGC) outputs from the Cell Search step 1. The ThresholdComparison block removes the noise components of the HGC outputs. Thecurrent FAT and RAKE finger locations are excluded from the Path Searchprocess. Then, the HGC outputs, except the current FAT and RAKE fingerlocations, are ranked in descending order. Finally, the largest paths,separated by at least 2 samples, are selected as new path candidates.

The primary synchronization code (PSC) is an unmodulated Golay sequenceof length 256 chips, repeated with a period of one slot. By detectingthe PSC, the User Equipment (UE) acquires slot synchronization to thetarget base station.

The path search procedures are as follows:

-   -   Step 1: Redo Cell Search Step 1;    -   Step 2: Detect {tilde over (P)}_(i) ^(HGC) above the first        threshold;    -   Step 3: Exclude the current RAKE locations;    -   Step 4: Exclude the current FAT finger locations;    -   Step 5: Rank {tilde over (P)}_(i) ^(HGC) in descending order;    -   Step 6: Find the new candidate list;    -   Step 7: Find the disappeared paths by comparing the old and new        RAKE locations;    -   Step 8: Finalize the candidate list (the new candidate list and        the disappeared paths).

FIG. 22 describes the path search process. The ‘star’ and ‘diamond’represent the current and old RAKE locations respectively. The shadedregion indicates the current FAT finger location. The current FAT andRAKE locations are excluded in the search process for the new pathcandidates. The HGC output powers are ranked in descending order and thelargest paths are selected as the candidates. In addition, thedisappeared paths are detected by comparing the old and current paths.The disappeared paths are also included as the path candidates becausethey may be present. Finally, five candidates are selected in the pathsearch process.

The path candidates selected in the search process should be verified.To verify the paths, the correlation powers of the corresponding codephases are obtained by the symbol-by-symbol integration between thereceived signal and the Common Pilot Channel (CPICH). If the correlationpower is greater than the second threshold, then the corresponding codephase is considered as a true path. Path Verification Procedures are asfollows:

-   -   Step 1: Measure the correlation powers, P_(i) ^(PN), of the new        candidates using CPICH    -   Step 2: Detect P_(i) ^(PN) above the second threshold    -   Step 3: Rank them in descending order        -   Step 4: Select the largest paths

The path verification process is illustrated in FIG. 23. The top rowshows the search process and the bottom row shows the verificationprocess. In the bottom row, there are new detected paths and olddetected paths. The powers and their indices are sent to the PathSelector to rank them in descending order. Finally, the largest pathsare reassigned to the RAKE fingers.

The computed correlation powers in the verification process are morereliable than the HGC correlation outputs since the former is computedwith 15-symbol integration but later is computed with 50-symbolintegration in a frame.

After comparing the powers of the current paths and the new pathcandidates, the largest paths are reselected and reassigned to the RAKEfingers. The path selector process is disclosed in FIG. 24. Threecurrent paths are assigned at the 2^(nd), 3^(rd), and 5^(th) RAKEfingers. The 4^(th) and 5^(th) of the current paths are de-allocated.Two new path candidates are assigned at the 1^(st) and 4^(th) RAKEfingers. The 3^(rd), 4^(th), and 5^(th) of the new path candidates arenot used.

Consider the situation where two paths are assigned to two separate RAKEfingers. Suppose that after some time the two fingers converged to thesame location. In such a case the RAKE controller needs to discard oneof the paths, free up the RAKE finger allocated to that path, inform thecontroller that a new finger has freed up, and instruct the pathsearcher to find a new path to be assigned. The RAKE controller shouldbe aware of the activity of every finger and control the overall RAKEreceiver, including the fingers.

FIG. 25 shows the probability of detection performance of Case 1 (SlowMoving Channel: 3 km/h) with various values of input SNR. The solid linewith circles represents the detection performance of the first path inthe RAKE allocation process. The dashed line with rectangles representsthe detection performance of the second path in the RAKE allocationprocess. The dash-dotted line with diamonds represents the detectionperformance of the second path after Relocation process. The detectionperformance is increased by 3-9%. This implies that in the event thesecond path is lost in the RAKE allocation process, the RAKE Relocationprocess is often able to recover it.

FIG. 26 shows the probability of detection performance of Case 5 (FastMoving Channel: 50 km/h) with various values of input SNR. The solidline with circles represents the detection performance of the first pathin the RAKE allocation process. The dashed line with rectanglesrepresents the detection performance of the second path in the RAKEallocation process. The dash-dotted line with diamonds represents thedetection performance of the second path after Relocation process. Thedetection performance is increased by 8-12%. The simulation results showthat the RAKE Relocation works better in the fast moving channel. Thisillustrates that the RAKE Relocation significantly helps to recover alost path in the fast moving channel.

FIG. 27 shows the probability of detection performance of Case 5 withvarious values of input SNR. Here, the minimum required ΔSNR is 0.4 dB.The detection performance of the second path is increased. Notice thatthe probability of false alarm is also slightly increased in this case.

The channel condition of the birth-death propagation is a non-fadingpropagation channel with two paths. The moving propagation condition hastwo paths that alternate between birth and death. The positions at whichthe paths appear are randomly selected with an equal probability and areshown in FIG. 28. The birth-death propagation conditions are as follows:

-   -   Step 1: Two paths, Path 1 and Path 2 are randomly selected from        the group ([−5, −4, −3, −2, −1, 0, 1, 2, 3, 4, 5]μs). The paths        have equal magnitudes and equal phases.    -   Step 2: After 191 ms, Path 1 vanishes and reappears immediately        at a new location randomly selected from the group but excludes        the point Path 2. The magnitudes and the phases of the tap        coefficients of Path 1 and Path 2 shall remain unaltered.    -   Step 3: After an additional 191 ms, Path 2 vanishes and        reappears immediately at a new location randomly selected from        the group but excludes the point Path 1. The magnitudes and the        phases of the tap coefficients of Path 1 and Path 2 shall remain        unaltered.    -   Step 4: The sequence in Step 2 and Step 3 is repeated.

FIG. 29 shows the simulation results for the PSC channel responses of100 frame runs (1 sec). Here, the input SNR is 10 dB. There aretransitions (birth and death) at every 191 ms. Since the two paths havedominant peaks, it is easy to detect without fading interference. Inthis figure, the largest path is time-aligned at zero relative delay.The detection and false alarm performances turn out to be P_(D)=1.0 andP_(FA)=0.0017, respectively.

FIG. 30 shows the simulation results for the CPICH channel responses of100 frame runs (1 sec). There are transitions (birth and death) at every191 ms. Two paths are dominant and easily detected without fadinginterference. The largest path is time-aligned at zero relative delay.In the static channel (AWGN), if the given SNR is higher than 5 dB weexpect perfect detection. The detection and false alarm performancesturn out to be P_(D)=1.0 and P_(FA)=0.0017, respectively.

-   -   1. The overall acquisition time is significantly reduced since        the dwell time (integration time) is saved. If there is only a        verification process, then the dwell time turns out to be about        0.66 sec. If the PSC is used in the initial path search process,        then the dwell time is reduced to 0.20 sec. The improvement of        the system speed is more than 3 times.    -   2. For the Relocation, it is easy to redo the path search        process to increase the detection performance. It requires extra        0.20 sec, but is still faster than a verification process        without path search process.

An alternative embodiment of the present invention utilizes time diverseintegration. To overcome slow fading effects, the consecutive symbolintegration, disclosed in the preferred embodiment, is modified to timediverse integration. The conventional integration to get the PNcorrelation power is done by consecutive symbol integration. However inthe slow fading channel, deep fading in the integration region causeslow detection probability. To mitigate this problem time diverseintegration can be used. As set forth above at equation 7, theconventional PN correlation power is computed over a predeterminednumber, for example 50, of consecutive samples as: $\begin{matrix}{{P_{k}^{PN} = {\sum\limits_{m = 0}{\quad{\sum\limits_{n = 0}^{N - 1}{{r( {{Nm} + n} )}{c^{*}( {{Nm} + n - k} )}}}}}},\quad{m =  0arrow 49. }} & (34)\end{matrix}$

Time diverse integration, for example, is represented as:$\begin{matrix}{{P_{k}^{PN} = {\sum\limits_{m \in I}{\quad{\sum\limits_{n = 0}^{N - 1}{{r( {{Nm} + n} )}{c^{*}( {{Nm} + n - k} )}}}}}},} & (35) \\{{{where}\quad{I}} \leq 150} & \quad\end{matrix}$

I is a selected index set that preferably has no more than 150 elements,e.g. I={0, . . . ,9, 50, . . . , 69, 100, . . . , 199}. The selection ofthe index set I is made to evaluate correlation power of samples overseveral different time intervals thus providing time diversity. Thecalculation of time diverse integration may also be modified andsimplified as conventional integration as discussed above with respectto equations 8 through 11.

In general, where communication signals are processed based in part onrelative power of signal samples, time diversity can be used tocalculate relative power as a function of values corresponding to timediverse signal samples. Preferably, a buffer is provided which stores atleast values r(r) that correspond to signal samples S_(r) that define aset R of samples. R is a subset of X consecutively received signalsamples S₀ through S_(X−1) that correspond to values r(0) throughr(X−1). The number of elements of subset R is less than X such that Rcontains at least two mutually exclusive subsets of consecutive samples{S₀ through S_(i)} and {S_(j) through S_(x−1)} Accordingly, R does notinclude sample S_(i+1) or S_(j−1). For convenience the buffer may storeall values r(0) through r(X−1), but a substantially smaller buffer canbe used if only the time diverse subsets of values represented by sampleset R are stored.

A processor is operatively associated with the buffer for calculatingrelative sample power based on values r(r) that correspond to signalsample elements S_(r) of the selected subset R of X consecutivelyreceived signal samples. Values of samples not contained in R, such asvalues r(i+1) or r(j−1) that correspond to signal sample elementsS_(i+1) and S_(j−1), respectively, are not used in the calculation.Accordingly, relative power is calculated based on sample seriesrepresenting at least two diverse time intervals.

Each pair of consecutive samples represents a sampling time interval tthat corresponds to the sampling rate used in obtaining samples of areceived signal. Preferably, at least two mutually exclusive subsets ofthe X consecutive samples exist that contain at least consecutivesamples {S_(i+1) through S_(i+51)} and {S_(j−51) through S_(j−1)},respectively, and do not contain any elements of subset R. In such case,subset R is defined by at least three mutually exclusive subsets ofconsecutive samples, which represent groups of consecutive samplesmutually offset in time by at least 50 times t. In the above example ofEquation 35 where N is 256 (the symbol size used in the CPICH), andI={0-9, 50-69, 100-199}, P_(k) ^(PN) is determined for a small set ofsamples S_(k) from values corresponding to the time diverse sampleseries {S₀ through S₂₅₅₉}, {S₁₂₈₀₀ through S₁₇₉₁₉} and {S₂₅₆₀₀ throughS₅₁₁₉₉} of the larger set of 51,200 samples {S₀ through S₅₁₁₉₉} whichcontains samples S_(k). Where samples are generated at a rate of onesample per chip duration, this represents time diversity of more than7000 chips between each of the three sample series upon which the powercalculations are based.

The time diverse integration can play an important role for probabilityof detection and false alarm performance. FIG. 31 shows that timediverse integration increases the detection performance 44% to 79% at 5dB SNR with respect to consecutive symbol integration for example. Inthis case, 35% of detection performance is increased. At 10 dB SNR 19%of detection performance is increased.

FIG. 32 shows that time diverse integration increases the detectionperformance 4% to 41% at 5 dB SNR. In this case, 37% of detectionperformance is increased. At 10 dB SNR 24% of detection performance isincreased. The RAKE relocation helps to increase the detectionperformance. However, it also slightly increases false alarms. Toachieve a high detection probability, ΔSNR should be controlledproperly, especially at high SNR.

FIG. 33 shows the probability of false alarm. If only a threshold testis used for the code phase detection, then the false alarm probabilityis increased as SNR increased. On the other hand, the false alarmprobability decreases with the preferred additional SNR test mentionedabove for RAKE finger allocation.

The modifications help to increase the detection performance andmitigate the slow fading effect, especially at low SNR case. Theadditional SNR test helps to reduce the false alarm at high SNR case.More investigation is required to get the best system performance.

With the fixed threshold test, one expects constant false alarm rate(CFAR). But simulation results (threshold test only in FIG. 33) showthat as input SNR increases, the false alarm probability is alsoincreased. In this example, the signal power is fixed but the noisepower is varying for controlling input SNR, i.e., high input SNR implieslow noise power with fixed signal power. Thus, the estimated noise powerdecreases as SNR increases. Then the threshold becomes low as SNRincreases. If the threshold is set too low, there are more chances thatthe ambiguous correlation coefficients are crossing the threshold. Thiscauses more false alarm probability with high SNR.

FIG. 34 shows the probability of detection for the first path in themultipath fading Case 5. It shows that the time diverse integrationoutperforms conventional consecutive integration in the low SNR.

FIG. 35 shows that the time diverse integration gives higher detectionperformance than consecutive integration. At 5 dB SNR, the probabilityof detection of the time diverse integration is 51% higher thanconsecutive integration. Furthermore, relocation process increases theprobability of detection even more.

FIG. 36 shows the probability of false alarm. The probability of falsealarm is decreased when using the additional SNR test. The relocationprocess generates a slightly higher probability of false alarm. Comparedto threshold test only, the additional SNR test helps to decrease theprobability of false alarm.

1. A method for allocating a Fat finger in a RAKE receiver whichprocesses communication signals, the method comprising the steps of:defining windows of a predetermined number of consecutive signal sampleswithin a set of samples that each have a first channel power and asecond channel power; calculating an average power of the window;determining whether the average first channel power of the window meetsa first threshold; if the average first channel power of a window meetsthe first threshold, then counting the number of samples in that windowwhose second channel power exceeds a second threshold; and if the countexceeds a preselected number, then allocating that window to the Fatfinger.
 2. The method according to claim 1 wherein the allocating stepincludes comparing the count to a lowest value; if the count meets thelowest value, then comparing the count to a required value; if the countmeets the required value, then allocating the window to the Fat finger;if the count does not meet the required value, then pruning closesignals in the window; counting the number of pruned signals in thewindow that meet the second threshold; and if the pruned count meets apredetermined acceptable level, then allocating the window to the Fatfinger.
 3. The method according to claim 2 wherein the steps areperformed by a wireless transmit receive unit (WTRU) configured for usein a wireless communication system.
 4. The method according to claim 2wherein the steps are performed by a wireless transmit receive unit(WTRU) configured for use in a 3rd Generation Partnership Project (3GPP)compliant code division multiple access (CDMA) wireless communicationsystem.
 5. The method according to claim 1 wherein the steps areperformed by a wireless transmit receive unit (WTRU) configured for usein a wireless communication system.
 6. The method according to claim 1wherein the steps are performed by a wireless transmit receive unit(WTRU) configured for use in a 3rd Generation Partnership Project (3GPP)compliant code division multiple access (CDMA) wireless communicationsystem.
 7. A method for allocating RAKE fingers for processingcommunication signals, based on a set of consecutive signal samples,where each sample has a first channel power and a second channel power,the method comprising the steps of: ranking the samples in descendingorder according to the first channel power of the samples; selecting theM largest first channel power samples that are at least two samplesapart; comparing the first channel power of each of the M samples with apredetermined threshold, to define a set of L samples, where M isgreater than or equal to L and where each of the L samples has a firstchannel power level greater than the predetermined threshold; selectingthe K largest second power level samples from the L samples, where L isgreater than or equal to K, where K is less than or equal to the numberof RAKE fingers to be allocated, and where the second channel powerlevel of each of the K samples exceeds a second power level threshold.8. The method according to claim 7 wherein the steps are performed by awireless transmit receive unit (WTRU configured for use in a wirelesscommunication system.
 9. The method according to claim 7 wherein thesteps are performed by a wireless transmit receive unit (WTRU)configured for use in a 3rd Generation Partnership Project (3GPP)compliant code division multiple access (CDMA) wireless communicationsystem.